{
 "metadata": {
  "name": "tamano del espectro difractado"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pylab import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "2D"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "OJO, HAY QUE REVISAR ESTA PROPAGACION DE ESPECTRO ANGULAR PQ ESTA EN LA MALA--- no sirve en este caso habia un error en el tama\u00f1o del pixel de entrada --- hay que comparar el ancho del primer pico con el criterio de Raigleigh --- este nos serviria para dar el ancho automatico a 200? 250 mm? de los ojos. -- hay que mirar si se puede separar los puntos de enfoque a cada uno de los ojos para mejorar lo que se observa --- con este criterio se podria decidir cuantas imagenes y de que resolusion se necesitan para un buen 3D"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def espectroAngular2(f,dx,dy,z,Lambda):\n",
      "\n",
      "\tM = f.shape\n",
      "\t[u,v] = mgrid[-M[0]/2:M[0]/2,-M[1]/2:M[1]/2]\n",
      "\tdu = 1./(M[0]*dx)\n",
      "\tdv = 1./(M[1]*dy)\n",
      "\tgaux = 1 - (Lambda*u*du)**2 - (Lambda*v*dv)**2\n",
      "\tgaux = where(gaux<=1, gaux, 0)\n",
      "\tk = 2*pi/Lambda\n",
      "\tG = exp( 1.j*z*k*sqrt( gaux ) )\n",
      "\tf1 = ifft2( fft2(f)*fftshift(G) )\n",
      "\treturn f1,dx,dy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def fresnel2(f,dx,dy,z,Lambda):\n",
      "\tM = f.shape\n",
      "\t[x,y] = mgrid[-M[0]/2:M[0]/2,-M[1]/2:M[1]/2]\n",
      "\tk=2*pi/Lambda\n",
      "\tdx2 = Lambda*z/M[0]/dx\n",
      "\tdy2 = Lambda*z/M[1]/dy\n",
      "\tw1 = exp(1.j*k/2/z*( (x*dx2)**2 + (y*dy2)**2 ))\n",
      "\tw2 = exp(1.j*k/2/z*( (x*dx )**2 + (y*dy )**2 ))\n",
      "\tf1 = fftshift( w1*fft2( fftshift(f*w2)))\n",
      "\treturn f1,dx2,dy2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### dimensiones de los pixeles del modulador\n",
      "M1=44.603\n",
      "M2=44.738\n",
      "R1=8./M1\n",
      "R2=8./M2\n",
      "R=(R1+R2)*.5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "############ Parametros\n",
      "M=500 ### Tamano de la matriz aumentada\n",
      "N=500\n",
      "d0=R #um\n",
      "Lambda=.550\n",
      "############\n",
      "u=zeros((M,N))\n",
      "T=5.\n",
      "d=T/R\n",
      "u[M/2-d/2:M/2+d/2,N/2-d/2:N/2+d/2]=1\n",
      "figsize(10,10)\n",
      "imshow(u, extent=[-M/2.*R,M/2.*R,-N/2.*R,N/2*R])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "<matplotlib.image.AxesImage at 0x1866a390>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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8wMBA7Ny5M5qamqJSqcSqVavi+PHjqSEBABarWV+ZupxTp07FrbfeWvu5paUlxsfHL/HI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\nQUwBACSIKQCABDEFAJDw/wFsWsMsFqOBHQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#rec,d,d=espectroAngular2(u,T,T,250000,Lambda)\n",
      "rec,d,d=fresnel2(u,R,R,250000,Lambda)\n",
      "print d\n",
      "figsize(10,10)\n",
      "imshow(abs(rec), extent=[-M/2.*d/1000.,M/2.*d/1000.,-N/2.*d/1000.,N/2*d/1000.])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1535.54493136\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "<matplotlib.image.AxesImage at 0x313e4d10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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//WkAwLlz5/DJT34Sr7zyCr73ve/hySefBAA8+eSTeOaZZwAAzz77LJ544glM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jX2gPo6zTXs9mi9J4pGXWPPp+NzK1ev/hxMdZZ2vK3cqTrgc7DU8Ro8IdSfV2aPJ2+X6oCH0oBY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\niRuHPk/4T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sEXvvAFNM3sjuPzn/88vv3tb+PSpUu4dOkSHnnkkdVKL46BPhuPAQ53ZF1j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\nVQkvtBhjjDHGquQ/jNf+jlQtk/kAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "1D"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "OJO, HAY QUE REVISAR ESTA PROPAGACION DE ESPECTRO ANGULAR PQ ESTA EN LA MALA"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def espectroAngular1(f,dx,z,Lambda):\n",
      "\tM =len(f)\n",
      "\tu = arange(-M/2,M/2)\n",
      "\tdu = 1./(M*dx)\n",
      "\tgaux = 1 - (Lambda*u*du)**2 \n",
      "\tgaux = where(gaux<=1, gaux, 0)\n",
      "\tk = 2*pi/Lambda\n",
      "\tG = exp( 1.j*z*k*sqrt( gaux ) )\n",
      "\tf1 = ifft( fft(f)*fftshift(G) )\n",
      "\treturn f1,dx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def fresnel1(f,dx,z,Lambda):\n",
      "\tM =len(f)\n",
      "\tx = arange(-M/2,M/2)  \n",
      "\tk=2*pi/Lambda\n",
      "\tdx2 = Lambda*z/M/dx\n",
      "\tw1 = exp(1.j*k/2./z*( (x*dx2)**2 ))\n",
      "\tw2 = exp(1.j*k/2./z*( (x*dx )**2 ))\n",
      "\tf1 = ifftshift( w1*ifft( fftshift(f*w2)))\n",
      "\treturn f1,dx2\t"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### dimensiones de los pixeles del modulador\n",
      "M1=44.603\n",
      "M2=44.738\n",
      "R1=8./M1\n",
      "R2=8./M2\n",
      "R=(R1+R2)*.5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "############ Parametros\n",
      "M=10000 ### Tamano de la matriz aumentada\n",
      "d0=R #um\n",
      "Lambda=.550\n",
      "############\n",
      "\n",
      "u=zeros(M)\n",
      "T=5.\n",
      "d=T/R\n",
      "u[M/2-d:M/2+d]=1\n",
      "figsize(25,2)\n",
      "\n",
      "s=arange(-M/2,M/2)*d0\n",
      "plot(s,u)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "[<matplotlib.lines.Line2D at 0x28afbd0>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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ls59NWlqOdRVvD4Wxg5PjNxkZGUlzc3MGBweTJDfddFNaWlqyatWq8TGdnZ15/fXX8/nP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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x287ecd0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#rec,d=espectroAngular1(u,T,250000,Lambda)\n",
      "rec,d=fresnel1(u,R,250000,Lambda)\n",
      "figsize(25,2)\n",
      "#xlim(0,M*d0)\n",
      "s=arange(-M/2,M/2)*d/1000.\n",
      "plot(s,abs(rec))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7fc25c189a90>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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s9esn/f3vdlPEF5yzwDgAAABqjp+r6iMXa0F1nggJAABwqnFOSkqyB9EtXixt\n2mSft20rRUZaj+A//tECrpVN9+9/l1591Yba8Bb0XbvWeteef75NW17QefVq6/G6bFnpQ3x4s2uX\n1LevNHeujWN8siuvlK66yh66WF3jxklnn229sg8elM46S9q714YSaSj+53/sJsO4cRa87d7dtmF1\nx9Xet8/G7l671sbvPlFhoXTBBdJ//7cUG1u5dJ2zB1c2aiS9+Wb508+fb2XvwQctcF5WD/Ft26Q7\n77T9N2vWr/NcEd9+a78o+PJL66FeUCC1a2fDqlxzjXTTTdKZZ1Y+XQAAgFNZdWK59KQGAADwkYIC\n64H7+OPSf/yH9RL9/e+lCRMssLxvX9XTXrhQioqy4FyPHtKHH9rQDvn51kv5P/9T+uQTqUsX6R//\nqHjPz6NHLeD2xRcWUC6vV3KfPtbjescOG2M6L6/saXNzpTFjpGnTKh+glqQOHazH7Lhxv+65vX69\ntGKFbWdfGDZMWrDA/l++3HrjNqQAtWQ3DpYts//nz7eHHfriwY9t2th2fuGFX3/37rv29/bbK5+u\nn5/dGElNLT9IPXu25eGzz6w8eBvCJCjIepFfdZX1hj+xd3l5vv9euvRSu9nTqZMNT3P4sB1P330n\n3XabBa67drWHSlZ1vPbCQmnRInuQ5E032Rjso0dL991n65qTU7V0AQAAGip6UgMAgHrFOev9efCg\nBX3bt7cgWXXG1ZUsKLR0qfVEXrVK+uknCzCdeaYUEmI9ka+6SjrnnMqnfeCAFB9vAbemTS3g2aeP\n9bw8dkz64QcLSH3zjXT11dIDD9iwGRWxf790zz1ScrL05JMWyPL30s1g9Wrr0dqihQXGO3Yse9pj\nx6xXaIsW0jvvlD9W9ImOH7cAfOvWFqgsLU+33WZB9Lfeqni6JysstEDjPfdY4NDjllss8P3Xv1Y9\n7RMdPGg9qX/5xYKxe/dKzzzjm7Rry4YN0uWXSxkZ1rM5MtJ6FPvCtm3Wi/iHH6RWreyzAwdsiJFP\nP7UbKFW1fr00dKgdm6UdF19+aYHc+fPt5kFl/POfdmPnk0+sx3dZnLMHRE6dasHn227z/iuBLVus\n1/1339nwN0OHViw/hw9bPfHii7Ydhw+3uqJNG/tu61arK5YutV8KPPCAjTdeWUeO2PZcuFBKT7c6\ntbDQ6tOQEGnIEAvGBwRUPu3S1mn3bvvbrJn9CsEXN0cAAEDDU51YLkFqAABOIXv2WA9Xz8PtmjWz\nXoXnnCN0vt4xAAAgAElEQVSddlrV03VO2rzZhpjYvNmWcfy4pXnOORb0uOACG9vXWwC1LOnp1kPy\niy8s6HPGGRa0kWxd/P2tl+iIETaWbYcOFU87K8se0jZjhqU5fLgF784914KyOTkW3FuyxIaV6NfP\nHto3YkT5gfH8fBu/+eGHrdfq+PHeg3X79kmvvWY9i//wBxtio3XrsqdfscJ6Nl95pfTUUxZMroiC\nAkv7jTesV2ZpecrLs57QjRpV7GF3pTlyxALyMTG2vBPNmiX97//aDYGWLSuf9omWLrWH7W3ebEOZ\n/PSTDWuxebMNdeIrQ4da4PHVV63X7ujRvku7NhQWSoGBFvSNjragbmio79K/6SZ7mOSkSfb+wQel\n7dult9+uftozZkjPPWe92E+8WbJunR1bH31kgdWqmDdPGjvW6pjSAtVHjthNj4wM+4VCUFDF0547\n13qR33mnlfdGjcqe9uOPLWA+aJA0ZYoF/cuSlWW9y599Vvrd76Snn67YsCWZmRYAf/ttO0aGDbMg\n91lnWT26e7eVj6+/tvp82DDL00UXVXyd8/Mt+P3RR1ZvZmRY8LtZs+KAdZcuVi+MHGl1blXql6ws\nC9ivW2c3SQ4etHVo29Z6skdEWDlv1qzyaZ9o3z67ObBvn9WLrVrZzb2goOqdNwEA+C2q0SB1cnKy\nYmNjlZ+fr3Hjxunuu+/+1TSTJ0/Whx9+qDZt2ui9995T6P+1hsua9+DBg7r55pu1evVq9e/fXzNn\nzlSLUq66CFKjIUpKSlJMTExdZwMNkHPSoUMW+CsosGBUdS+8TnT4sPTjj/bz++PHLdDTpo1dWH77\nbfXKbUGBtHOnXaju22fDB/j5Wfrt2tnQBBV9mFZp9u2zIOKmTdLPP9s6FBRY4KttWxt3NSyscoFL\nD+cs3ZUrLVCakWFBy8OHLc+tW9vFcFiYBTcqO7bp8eN2kb14sQVf09MtkJyTY4HYli3tYr5HD3sY\nWEyMrU9F7dlTHNxdtsyCLZ07237187NttW2bbcPISOniiy04WpHAlXPWK/e99yxw4+dnQaKwMFtG\n48Z2Qf/jjxb0WLLEln/99Tb0RESE9/Tz8y2Q+eqr1ivxuuvsJ+9RUb8OPP7yi6X/6acWFLrsMuth\nePBg2WV3/34LnL75pgUbx42zoI03x45Znp5+2gJlL75Y9sPz0tMt8NWypQXXylvfE2VnW0Br3jzb\nvoMH/3qaDz+U7rrLemhfe23F0z7RnDnWIzQhwXpNeuTnW8Dx8GELnFVnWIvdu22fPfNMcVB32zYr\nb59/XvpY0lUxapQFu+66S5o82cr2iy/6Jm2PRx+1ffPeexZcr8pYxhVRk22FK66woO4zz1gwv7q/\nQDjRqlX2a4MffrCAYffuVneed17103bOymSbNtIrr9hn+/dbINczJEZ1zJtnNx7mzSsZHN6/37ZZ\nUJAFyivzawKPn36yYW2aNrXj1tPT/MRl3H67BVv/8Y/Sj/eyHDpk9dErr0gPPWTB8NIC4Xv3So89\nZsHp226T7rij/GD7vn3S++9bIDwoyOrLCy8se/r9+6WJE5M0d26MunSxG0cxMVavnhjMPX7c6sfE\nROvB/sMP1rP/rrvs1wre5OTY8ffuu3ZeufBCG5c+ONh+9VJYaMFrz3n7++8tDzfeaOeQivTgPnDA\nziULFlhv8wMHLP3AQAum5+RYW2PPHuvlfskl9guYAQMqfjwdPCh99ZWNbb5ihdWJP/9sda+/v52j\nzz3XbopGRdkyyts2Jzt0yLbBd99JGzdaXZyTY/uiaVNLPzjY6uABAyr/rALJ2nMbNtj+/PlnKzMF\nBZZW27aWfvfu9reqdU1Bgd3sysy0/B86ZOm3aGHbpFu36reHv/46Sb16xSgry857BQVWnjztYF/V\nk8eOWTvo+HE7r555ZtVu3AMScQU0XDUapI6IiNALL7ygoKAgDR8+XIsXL1ZgYGDR96mpqZowYYLm\nzJmj+fPn67333tPcuXNLnXfJkiVq27atnn76ae3YsUPPPPOM7rvvPp133nmaOHGiT1essl5/3S6Q\nmzWzi81Onayx1rWrNVgqIzvbevZs3WqNqH37rKHSqJE1fFq2tN4MnpNuUFDlTl5Hjlhet2yxwMD+\n/fYqLLT0mzSxQM0551jjJCyscg3u/HxrSG3aZI2FffuKg06nn24Nn8BAy/8551igo2PHip/cnbNG\nzqZNtp327Cm5jRo1shO6Zxv16GE99CrTA2PvXmusbdpk22jvXluGc5Z/zzY6+2wLDvXqVbn9nJtr\n6W/YYAGtrCzb78ePS2lpcRowIE7t2tl2OfdcS78y63DsmOU9Pd3+7tpl6R88aNu5USNrVHXsaOsQ\nFma9ZNq3r1j6+flWftavt9fOnbYf9u61beTvb8G5jh3tFRpq69CjR8UauIWFdkHy/ff2+uEHazjv\n3m3bSLKGW4cOln63bpZ+7962vcorS85ZY3bNGnuI0/r1VqZ++cUats7ZNmrXzl6efdyrlzXUK9LQ\n/flnu6hYvdqWs327fZadbevnnF2EtmtnwTpP+hdcYPu6vHXIyrKA28qVdtG/ZYvtB+fsAqtRI1sX\nPz/bxz17WvqDBlmQ8OQL4JPl5NiF17JlNtbnunV2EXbOOdboP+MMq0v27bN1at48ToMHx+nCCy39\nAQO8l9fcXOtF5flJ8urVdrHSpYv9bdLE1mXfPiu/mzfb/h40yHqEXXKJ9/Fpjx61QEJiog2PsH27\nlcPu3W17tGxp5fTgQSu7mzZJaWl2XMTEWCDriivK7nV6/Lil//HHth7+/haI7NnT6v3WrW0dDh+2\nddiyxdJfvNi+u+IKuzgfNKj0+jsvz4LG77xj6YeG2npHRFhP4w4drJ7Ly7P98sMPxel/+aWtxx/+\nYA8IK2075efbeKuesVyHD7cA3gUX2D4orfzl5Ni+mj/ffv7eoYMFMW6++dfHREGBXcA/9ZRt35tv\ntgBRaGj5ZXvDBuuV+8Ybti0nTLBewCdup8JCC+T87W+2P8eNs8BXRevIQ4fsvP3cc1KzZnGaMydO\nPXqUTP+tt6yn4lVXWWCnshf/hYX20LS//MV6VE+dWhw4Lyy0ntl//7sFz2Jjq36BO3euBZTuvtuC\n1n5+lv5DD1mQ5tNPKz+8wckWLbLgzeuvW6ClsFD6r/+yOnPOnKoFLk62erX1yPzqKzsnDR1qQ4GU\n0ryrspUrbX9+950tY/lyK+++9O23tr9btrQ6uabExcUpLi6uRtJ+5BEbrmTECAv2+dpll9mwK5s2\n2bns//0/36Wdk2P15DPPWPm57jprk/vqZsQnn1iQd+FCq+/377dye/759uuG6gSq8vNtSJqvv7b6\n2VN3r11rN5muuMKCzVU93tLT7cGYBQVWv4aFFX83e7YFgK+6yvZ/ZW/Y5uVZWXnwQWsDPP10yWGP\njh61+vbZZ6VOneKUkBBX4eGKPHl/7TWr0266yW4wnntuyWl27rTx5xMSrIzdcovdVC0v6Lx3r51v\n3367eGz+O+4o/dy5ZInV3V98YW2FUaPsb9eupe/7Awes3pk3z24GHzsm3XqrBdxLGxbq6FHbFx9+\naG2wQYPshkRkpOXn7LPtPFd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       "text": [
        "<matplotlib.figure.Figure at 0x287e750>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "loop para comparar las graficas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M=20000\n",
      "figsize(25,2)\n",
      "\n",
      "D=250000\n",
      "\n",
      "for T in linspace(25,10,10):\n",
      "    u=zeros(M)\n",
      "    d=float(T)/R\n",
      "    u[M/2-d:M/2+d]=1\n",
      "    s_in=arange(-M/2,M/2)*d0/1000.\n",
      "    ###rec,d=espectroAngular1(u,T,250000,Lambda)\n",
      "    rec,d2=fresnel1(u,R,D,Lambda)\n",
      "    \n",
      "    s_out=arange(-M/2,M/2)*d2/1000.\n",
      "    \n",
      "    L=300 #um\n",
      "    figure(), plot(s_in,u), title('Pixel de %f um'%T),    \n",
      "    xlim(-5, 5)\n",
      "    #ylim(0,1)\n",
      "   \n",
      "    ### calculo usando la relacion de De Broglie\n",
      "    l=2*D*tan(1/2.*arcsin(1.22*Lambda/T))/1000.    \n",
      "    \n",
      "    figure(), plot(s_out,abs(rec)), title('rejilla de %f um'%l),    \n",
      "    xlim(-5, 5)\n",
      "    #xlim((M/2)*d0-L/2,(M/2)*d0+L/2)\n",
      "    ylim(0,0.016)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fc25eb31e10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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9etDdDADVQXa2maZjxQozDVLfvuY4PWqU6ZQGAAA3HwJoAABQbZ07JyUlmSBj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\nYjqkH3pIevBB0yUNAEBVKTe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       "text": [
        "<matplotlib.figure.Figure at 0x7fc25eb0dc10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x313ad10>"
       ]
      },
      {
       "output_type": "display_data",
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4uBQTI8XGSsOHEzQAQGt29Kj03nvOQLptWxNG33yz+WKSM2EAAGieaspy29S1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\nNe0rSQMGDGARQgCAJOnsWdN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       "text": [
        "<matplotlib.figure.Figure at 0x2b4ba90>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fc25c93f7d0>"
       ]
      },
      {
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2drbGjBnj2ic7O1tjx46VzWbTuHHj9PTTT1d7bFZWlsaMGaMOHTqouLhY5eXl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\nK3Wt9qnmoy9rPcNqAE2fn5+5CImK8lx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       "text": [
        "<matplotlib.figure.Figure at 0x3104d50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fc25c5c0790>"
       ]
      },
      {
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       "text": [
        "<matplotlib.figure.Figure at 0x3105a50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x2a9a190>"
       ]
      },
      {
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       "text": [
        "<matplotlib.figure.Figure at 0x32577d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fc25eb27e10>"
       ]
      },
      {
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       "text": [
        "<matplotlib.figure.Figure at 0x3188490>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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W6urq0rNnz/YuB+jABg0alBkzZqR3796pqqrKo48+mtLS0vYuC+jgGhoaMmHC\nhJSWluaHP/xhe5cDdDLz58/PP//zP+fuu+9u71Lo4LS8YLdm91GgLU2cODFvv/12jj/++CTJsGHD\nct1117VzVUBHNH369FRXV2fjxo2ZNGmSMBloE4899lhuvvnmDBw4MIMGDUqSTJ06NVVVVe1cGdBZ\nyFloC1YoAwAAAADQIrZ3BAAAAACgRQTKAAAAAAC0iEAZAAAAAIAWESgDAAAAANAiAmUAAAAAAFpE\noAwAAAAAQIsIlAEAAAAAaJH/D6S0bLcRZvjGAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3185a90>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fc25e945150>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fc25e634790>"
       ]
      },
      {
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       "text": [
        "<matplotlib.figure.Figure at 0x7fc25e5fdc10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fc25e0ff490>"
       ]
      },
      {
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       "text": [
        "<matplotlib.figure.Figure at 0x7fc25db02a90>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fc25dafa150>"
       ]
      },
      {
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       "text": [
        "<matplotlib.figure.Figure at 0x7fc25d3a7790>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "M=100000\n",
      "figsize(25,2)\n",
      "\n",
      "for T in [3]:#linspace(10,4,10):\n",
      "    u=zeros(M)\n",
      "    d=float(T)/R\n",
      "    u[M/2-d:M/2+d]=1\n",
      "\n",
      "    u[M/2-d+4*d:M/2+d+4*d]=1\n",
      "\n",
      "    s=arange(0,M)*d0\n",
      "    rec,d=espectroAngular1(u,T,250000,Lambda)\n",
      "\n",
      "    \n",
      "    L=300 #um\n",
      "\n",
      "    figure(), plot(s,u), title('Pixel de %f um'%T),    \n",
      "    xlim((M/2)*d0-L/2,(M/2)*d0+L/2)\n",
      "    ylim(0,1)\n",
      "    \n",
      "    figure(), plot(s,abs(rec)), title('Pixel de %f um'%T),    \n",
      "    xlim((M/2)*d0-L/2,(M/2)*d0+L/2)\n",
      "    ylim(0,1.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      },
      {
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      }
     ],
     "prompt_number": 220
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tan(arcsin(1.22*Lambda/25.))*250000\n",
      "1.22*Lambda/T*250000"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "167750.0"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}